The discretization filter: A simple way to estimate nonlinear state space models

State Space Models and the Kalman Filter

A Simple Way to Estimate the Cost of Downtime

It is time for the systems community of researchers and developers to broaden the agenda beyond performance. The 10000X increase in performance over the last 20 years means that other aspects of computing have risen in relative importance. We see three challenges for the future as [Patterson2002a]: 1. Synergy with humanity: We need to make the technology match human nature, both for users of pe...

Nonlinear state space smoothing using the conditional particle filter

To estimate the smoothing distribution in a nonlinear state space model, we apply the conditional particle filter with ancestor sampling. This gives an iterative algorithm in a Markov chain Monte Carlo fashion, with asymptotic convergence results. The computational complexity is analyzed, and our proposed algorithm is successfully applied to the challenging problem of sensor fusion between ultr...

System identification of nonlinear state-space models

This paper is concerned with the parameter estimation of a general class of nonlinear dynamic systems in state-space form. More specifically, a Maximum Likelihood (ML) framework is employed and an Expectation Maximisation (EM) algorithm is derived to compute these ML estimates. The Expectation (E) step involves solving a nonlinear state estimation problem, where the smoothed estimates of the st...

Hysteresis Identification using Nonlinear State-Space Models

Most studies tackling hysteresis identification in the technical literature follow white-box approaches, i.e. they rely on the assumption that measured data obey a specific hysteretic model. Such an assumption may be a hard requirement to handle in real applications, since hysteresis is a highly individualistic nonlinear behaviour. The present paper adopts a black-box approach based on nonlinea...